Enhancing Cybersecurity Posture With AI

Yesterday, the National Security Agency Artificial Intelligence Security Center published the joint Cybersecurity Information Sheet Deploying AI Systems Securely in collaboration with the Cybersecurity and Infrastructure Security Agency, the Federal Bureau of Investigation (FBI), the Australian Signals Directorate’s Australian Cyber Security Centre, the Canadian Centre for Cyber Security, the New Zealand National Cyber Security Centre, and the United Kingdom’s National Cyber Security Centre. Deploying AI securely demands a strategy that tackles AI-specific and traditional IT vulnerabilities, especially in high-risk environments like on-premises or private clouds. Authored by international security experts, the guidelines stress the need for ongoing updates and tailored mitigation strategies to meet unique organizational needs. 🔒 Secure Deployment Environment: * Establish robust IT infrastructure. * Align governance with organizational standards. * Use threat models to enhance security. 🏗️ Robust Architecture: * Protect AI-IT interfaces. * Guard against data poisoning. * Implement Zero Trust architectures. 🔧 Hardened Configurations: * Apply sandboxing and secure settings. * Regularly update hardware and software. 🛡️ Network Protection: * Anticipate breaches; focus on detection and quick response. * Use advanced cybersecurity solutions. 🔍 AI System Protection: * Regularly validate and test AI models. * Encrypt and control access to AI data. 👮 Operation and Maintenance: * Enforce strict access controls. * Continuously educate users and monitor systems. 🔄 Updates and Testing: * Conduct security audits and penetration tests. * Regularly update systems to address new threats. 🚨 Emergency Preparedness: * Develop disaster recovery plans and immutable backups. 🔐 API Security: * Secure exposed APIs with strong authentication and encryption. This framework helps reduce risks and protect sensitive data, ensuring the success and security of AI systems in a dynamic digital ecosystem. #cybersecurity #CISO #leadership

Most companies still follow the old cybersecurity playbook: 1. Buy antivirus 2. Trust the default firewall 3. Hope a data breach never happens 4. React chaotically when it does 5. Spend even more after damage is done The new, AI-driven cybersecurity approach flips this: 1. Proactively identify threats 2. Use AI for threat intelligence and gap analysis 3. Implement zero-trust architecture 4. Automate detection and response 5. Continuously refine with real-time data The hard truth? Most data breaches (and the resulting financial devastation) happen because organizations rely on outdated, reactive measures. But that was before AI. I’ve spent years mitigating breaches that could have been prevented with proactive measures. Now, with the right AI-driven framework, you can avert catastrophic threats in days, not months. Here’s my 5-step AI-enabled cybersecurity framework to save your company from hefty fines, lost trust, and public embarrassment: 1. Asset Discovery & Prioritization • Use AI-powered scanners (like Censys or Shodan) to find every exposed asset you have. • Feed the list into ChatGPT or other AI tools to categorize them by risk level. • If you don’t know what you’re defending, you’ve already lost. 2. Threat Intelligence & Gap Analysis • Tap into threat intel feeds (MITRE ATT&CK, VirusTotal, open-source repos). • Ask AI to compare your network or app vulnerabilities against known exploits. • No deep intel on emerging threats? That’s a glaring gap. 3. Automated Penetration Testing • Old approach: hire pen testers once or twice a year. • New approach: continuous AI-driven pentests that probe your environment 24/7. • If the AI tool cracks through your defenses easily, it’s time to upgrade your armor. 4. Zero-Trust Implementation • Grant “least privileged” access—no one gets more than they absolutely need. • Use AI to monitor user behaviors for anomalies (e.g., logging in from new locations, odd times). • Trust but verify. Actually, don’t trust—verify everything. 5. Incident Response Optimization • Replace static incident playbooks with AI-updated procedures. • Use machine learning to accelerate root cause analysis. • Automate common remediation steps. • If your IR plan is collecting dust in a binder, you’re already behind the curve. This isn’t just a few security patches—it’s a transformative shift. AI makes cybersecurity continuous, adaptive, and deeply data-driven. The result? • Fewer vulnerabilities slipping through the cracks • Faster response times for any incidents that do occur • Significantly reduced risk of financial and reputational damage You can keep plugging holes after breaches happen—or harness AI to build a virtually watertight security posture before it’s too late. … It’s your move. …

Enhancing Incident Response: The AI Advantage The landscape of Cybersecurity Incident Response (IR) is shifting. As threats become more automated and sophisticated, relying solely on manual processes is no longer a viable strategy for maintaining resilience. Integrating Artificial Intelligence into the IR lifecycle is transforming how organizations detect, contain, and recover from breaches. The Role of AI in the IR Lifecycle AI and Machine Learning (ML) are not just buzzwords; they are force multipliers for security operations centers (SOCs). * Accelerated Detection: AI models analyze massive datasets in real-time to identify anomalies that deviate from established baselines, often catching "living off the land" attacks that bypass traditional signature-based tools. * Automated Containment: Through Security Orchestration, Automation, and Response (SOAR), AI triggers immediate playbooks—such as isolating an infected endpoint or revoking compromised credentials—reducing the "breakout time" for attackers. * Intelligent Recovery: Post-incident, AI helps prioritize system restoration based on criticality and ensures that backups are clean of dormant malware, preventing a "re-infection" cycle. Key Strategic Benefits The integration of AI provides several critical advantages for technical teams: * Significant Noise Reduction: AI filters out false positives and aggregates related alerts, allowing analysts to focus their expertise on high-fidelity threats rather than "alert fatigue." * Predictive Path Modeling: By analyzing historical data and current environmental changes, ML models can predict potential attack paths before the adversary reaches their objective. * Cross-Layer Data Correlation: AI automatically links disparate events across network, cloud, and host layers, providing a holistic view of the "blast radius" that would take humans hours to piece together. * Continuous Adaptive Learning: Every incident provides data that retrains the models, ensuring the defense evolves alongside the ever-changing threat landscape. Moving Toward Proactive Defense: The goal of AI in cybersecurity isn't to replace the human element but to augment it. By automating the repetitive, high-volume tasks of detection and initial triage, seasoned professionals can focus on complex threat hunting and strategic recovery efforts. In an era where every second counts, AI provides the speed and scale necessary to stay ahead of the adversary. #Cybersecurity #ArtificialIntelligence #IncidentResponse #Infosec #SOAR #ThreatIntelligence #DataSecurity #TechLeadership #MachineLearning #CyberDefense

The National Institute of Standards and Technology (NIST) has released a draft of its “Cybersecurity Framework Profile for Artificial Intelligence” (open for public comment until Jan 30, 2026) to help organizations think about how to strategically adopt AI while addressing emerging cybersecurity risks that stem from AI’s rapid advance. Building on the #NIST Cybersecurity Framework 2.0, the Cyber AI Profile translates well-established risk management concepts into AI-specific cybersecurity considerations, offering a practical reference point as organizations integrate AI into critical systems and confront AI-enabled threats. The Cyber AI Profile centers on three focus areas: • Securing AI systems: identifying cybersecurity challenges when integrating AI into organizational ecosystems and infrastructure. • Conducting AI-enabled cyber defense: identifying opportunities to use AI to enhance cybersecurity, and understanding challenges when leveraging AI to support defensive operations. • Thwarting AI-enabled cyberattacks: building resilience to protect against new AI-enabled threats. The Profile complements existing NIST frameworks (CSF, AI RMF, RMF) by prioritizing AI-specific cybersecurity outcomes rather than creating a standalone regime.

Today, NIST released the initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile), a community profile built on NIST CSF 2.0 to help organizations manage cybersecurity risk in an AI-driven world. A key section of this draft is Section 2.1, which introduces three Focus Areas that explain how AI and cybersecurity intersect in practice: 1. Securing AI System Components (Secure) AI systems introduce new assets that must be secured; models, training data, prompts, agents, pipelines, and deployment environments. This focus area emphasizes treating AI components as first-class cybersecurity assets, integrating them into governance, risk assessments, protection controls, and monitoring processes. It reinforces that AI risk should not be siloed from enterprise cybersecurity risk management. 2. Conducting AI-Enabled Cyber Defense (Defend) AI is not just something to protect, it is also a powerful defensive capability. This area focuses on using AI to enhance detection, analytics, automation, and response across security operations. At the same time, it recognizes the risks of over-reliance on automation, model integrity concerns, and the need for human oversight when AI supports security decision-making. 3. Thwarting AI-Enabled Cyber Attacks (Thwart) Adversaries are increasingly using AI to scale phishing, evade detection, and automate attacks. This focus area addresses how organizations must anticipate and counter AI-enabled threats by building resilience, improving detection of AI-driven attack patterns, and preparing for a rapidly evolving threat landscape where AI is weaponized. Why This Matters Together, Secure, Defend, and Thwart provide a practical structure for aligning AI initiatives with existing cybersecurity programs. By mapping AI-specific considerations to CSF 2.0 outcomes (Govern, Identify, Protect, Detect, Respond, Recover), the Cyber AI Profile helps organizations integrate AI security into familiar risk management practices. This is a preliminary draft, and NIST is seeking public feedback through January 30, 2026. If your organization is building, deploying, or defending with AI, now is the time to review and contribute. 🔗 https://lnkd.in/e-ETZXH8

Navigating AI-Driven Cybercrime: What Every Business Needs to Know Here’s the deal: The rise of AI isn’t just transforming industries—it’s transforming cybercrime too. Staying secure in this new landscape means understanding just how AI is reshaping threats. Here are three critical insights to keep your business one step ahead: → AI is Empowering Cybercriminals From automated phishing to deepfake scams, cybercriminals are using AI to make their attacks faster, smarter, and more convincing. Traditional defenses alone won’t cut it. Staying informed about AI-driven threats is crucial. → Strengthen Your Cybersecurity Practices Don’t wait for an attack to hit. Implement robust measures—multi-factor authentication, regular updates, and AI-powered security tools that can detect suspicious activity in real time. Empower your employees with training to recognize phishing attempts and scams. → Use AI as a Defense Tool, Not Just a Threat AI can be your ally too. Leverage machine learning to spot patterns, monitor activity continuously, and respond automatically to threats. Shifting from a reactive to a proactive approach is key in today’s threat landscape. The takeaway? The AI-driven cyber threat landscape is here, and it’s only growing. Businesses that understand, prepare, and harness AI for defense will be best positioned to stay secure. Are you ready to strengthen your defenses? Let’s talk strategy.

As we step into 2025, I wanted to start the year sharing something that’s reshaping cybersecurity in real-time—and will only become more critical as we move forward: countering AI with AI. Attackers are evolving, using AI to reshape malicious scripts into forms that evade detection. These aren’t entirely new threats; they’re familiar tactics, reworked with precision to outsmart defenses. The challenge lies in how subtle these changes can be. AI allows attackers to modify scripts in ways that appear deceptively benign—renaming variables, inserting dead code, or altering structures—while preserving malicious intent. It’s a strategic evolution with significant implications for every industry navigating today’s digital landscape. Yet, the same AI that attackers use to refine their strategies can be their own undoing. By harnessing AI’s capabilities, defenders can anticipate these shifts and ensure the balance tips in their favor. It’s a case of innovation meeting innovation. In this context, adversarial machine learning offers a promising solution. It 𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝘀 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝘀𝗮𝗺𝗽𝗹𝗲𝘀, enabling them to recognize obfuscation tricks used in AI-rewritten malicious scripts. By harnessing adversarial machine learning at Palo Alto Networks, we have significantly improved our Advanced URL Filtering. And the proof is in the pudding—a 10% boost in real world detection rate! We have essentially turned the attackers’ own tools against them, ensuring we stay ahead in an ever-shifting landscape. Tackling challenges like these reminds me why I love this field—it’s fast-paced, deeply complex, and constantly evolving. If you’re as intrigued as I am about how AI is reshaping cybersecurity, I highly recommend Unit 42’s recent article on this fascinating challenge: https://lnkd.in/g-Eg2usB 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗼 𝗸𝗶𝗰𝗸𝗶𝗻𝗴 𝗼𝗳𝗳 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗯𝗼𝗹𝗱 𝗶𝗱𝗲𝗮𝘀, 𝗿𝗲𝗹𝗲𝗻𝘁𝗹𝗲𝘀𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗸𝗲𝗲𝗽 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗵𝗮𝗿𝗴𝗲. #HappyNewYear #CounterAIWithAI #AI #Cybersecurity Image Credit: Palo Alto Networks Unit 42

As a CDIO, cybersecurity keeps me up at night. This week, the game changed. AI just became both the most dangerous weapon and the most powerful shield in cybersecurity history. Simultaneously. And this one deserves more than a quick scroll past. This week Anthropic announced Claude Mythos — deemed too dangerous to release publicly. It autonomously found thousands of critical vulnerabilities across every major OS and browser, including a flaw undetected for 27 years. The timeline for finding exploits collapsed from months to seconds. OpenAI responded within days with GPT-5.4-Cyber. Same technology. Opposite bets on how to deploy it safely. Here's what this means if you're leading technology right now: The threat is real and it's here. Nation state actors are already using AI to infiltrate organizations — tech companies, banks, government agencies — before anyone notices. The average US breach now costs $10.22 million and goes undetected for 241 days. Eight months of attackers inside your walls before you even know they're there. But the defenders who move fast get there first. Organizations using AI in their security stack are reducing breach costs by $1.9 million per incident. The same technology cutting both ways. As a CDIO, my four considerations right now: Cyber hygiene is existential — patching, access controls, logging. Mythos specifically exploits weak posture. The basics still save you. Our biggest vulnerability is still human — 68% of breaches involve human error or social engineering. AI supercharges phishing. Train relentlessly. AI in our security stack is no longer optional — if your attackers use AI and your defenders don't, that's not a gap. It's surrender. Govern your shadow AI — nearly 1 in 5 breached organizations traces back to unsanctioned AI tools, adding $670K to the cost of each incident. Know what your teams are running before it runs you. The weapon exists. The shield exists. Both are AI. The only question is which one reaches your organization first. How are you thinking about this? 👇 #Cybersecurity #AI #CDIO #TechLeadership

𝐀𝐫𝐞 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 , 𝐀𝐰𝐚𝐫𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐬 𝐭𝐡𝐞𝐢𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐚𝐫𝐫𝐲? AI increases the pace of business. With that it also increases the attack surface. If AI affects your data, decisions or workflows, The risks associated with i are now business risks. Leaders do not have to build models. They need to understand where models fail. I am sharing the 𝟏𝟎 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬, Every leader should understand. 𝟏-𝐃𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲  AI sees customer data, internal docs and logs. Know what data is used and who can access it. 𝟐-𝐌𝐨𝐝𝐞𝐥 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠  Bad data can quietly change model behaviour. Ask how training data is protected. 𝟑-𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧  Inputs can trick models into breaking rules. Controls must exist outside the model. 𝟒-𝐎𝐮𝐭𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 𝐥𝐞𝐚𝐤𝐚𝐠𝐞  Models can repeat sensitive information. Set strict rules on what enters AI tools. 𝟓-𝐈𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐚𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬  AI agents run with powerful keys. Least privilege is critical. 𝟔-𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐚𝐧𝐝 𝐓𝐡𝐢𝐫𝐝 ‑ 𝐏𝐚𝐫𝐭𝐲 𝐌𝐨𝐝𝐞𝐥𝐬  Third party models can hide vulnerabilities. Security reviews still apply. 𝟕-𝐑𝐨𝐛𝐮𝐬𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 𝐟𝐨𝐫 𝐀𝐈  Dashboards miss behaviour changes. Expect visibility into inputs and outputs. 𝟖-𝐀𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐀𝐭𝐭𝐚𝐜𝐤𝐬 𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬  Small changes can cause wrong results. High risk use cases need extra testing. 𝟗-𝐀𝐈 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬  Policies define ownership and escalation. Frameworks reduce chaos. 𝟏𝟎-𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬  Know how to pause, roll back and communicate. Treat AI incidents like cyber incidents. AI is not just a productivity tool. Now it is part of your security perimeter. Which of these areas would you prioritize for deeper understanding? --------- Hi, I'm Harris D. Schwartz, Fractional CISO and Cybersecurity Leader. I help CEOs and executive teams strengthen their security posture and build resilient, compliant organizations. With 𝟑𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝐍𝐈𝐒𝐓, 𝐈𝐒𝐎, 𝐏𝐂𝐈, 𝐚𝐧𝐝 𝐆𝐃𝐏𝐑, I know how the right security decisions reduce risk and protect growth. If you are planning how your security program needs to evolve in 2026, this is the right time to have that conversation. #CyberSecurity #AISecurity #AIrisk #CISO #SecurityLeadership #CyberRisk

𝐀𝐈 𝐢𝐧 𝐂𝐲𝐛𝐞𝐫𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 𝐀 𝐃𝐨𝐮𝐛𝐥𝐞-𝐄𝐝𝐠𝐞𝐝 𝐒𝐰𝐨𝐫𝐝 🛡️ Recent research from Google DeepMind reveals how frontier AI models could disrupt the economics of cyberattacks, lowering barriers for adversaries and amplifying risks across the attack chain. Key insights: • Automation at Scale: AI enables attackers to automate reconnaissance, weaponization, and evasion, making sophisticated attacks accessible to less-skilled actors. • New Threat Vectors: From crafting polymorphic malware to orchestrating long-term cyber campaigns, AI introduces novel risks that traditional defenses struggle to counter. • Underestimated Phases: The study highlights AI’s potential in evasion, obfuscation, and persistence - critical yet often overlooked stages of the attack lifecycle. While current AI models lack the capability for end-to-end cyber operations, their ability to enhance specific phases is undeniable. This means adapting strategies to target emerging vulnerabilities and prioritize defenses where AI-driven disruptions are most likely. 🔒 What’s Next? 1. Conduct threat coverage gap assessments using structured frameworks like MITRE ATT&CK. 2. Invest in red-teaming that emulates AI-enabled adversary behavior. 3. Deploy targeted mitigations filtering misuse, fine-tuning models, and evolving response protocols. 🥷🏼 The path forward requires vigilance and innovation. As AI progresses, its impact on cybersecurity will only grow. Let’s stay ahead of the curve. #CyberSecurity #CISO